Efficacy and safety of pharmacological treatments for Lyme neuroborreliosis: An updated systematic review

BACKGROUND

Evidence-based recommendations for treatment of Lyme neuroborreliosis (LNB) should rely on the available literature. As new data emerges, close review and evaluation of the recent literature is needed to build evidence-based recommendations to inform clinical practice and management of LNB. We performed an update of a previous systematic review on treatment of LNB.

METHODS

A systematic literature search of Medline and CENTRAL was performed for published studies from 2015 to 2023 to update a previous systematic review. Randomized controlled trials (RCTs) and non-randomized studies (NRS) were evaluated. Risk of bias was assessed using the Cochrane risk of bias tools for RCTs; NRS were assessed using the ROBINS-I-tool. Quality of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Data were integrated into an existing meta-analysis of the available literature.

RESULTS

After screening 1530 records, two RCTs and five NRS with new and relevant data were additionally identified. Meta-analysis showed no statistically significant difference between doxycycline and beta-lactam antibiotics regarding residual neurological symptoms after 12 months. Meta-analysis showed no benefit of extended antibiotic treatment of LNB. Three NRS show no benefit for additional steroid use in LNB with facial palsy.

DISCUSSION

Additional incorporated recent research corroborates existing guideline recommendations for treatment of LNB. New RCTs add to the certainty of previous analysis showing similar efficacy for doxycycline and beta-lactam antibiotics in LNB. Available evidence shows no benefit for extended antibiotic treatment in LNB. NRS do not suggest a role for steroids in facial palsy due to LNB.

Empirical evaluation of the methods used in systematic reviews including observational studies and randomized trials

OBJECTIVE

To examine the methodological characteristics of systematic reviews and meta-analyses including observational studies (OSs) and randomized controlled trials (RCTs), in various medical disciplines.

STUDY DESIGN AND SETTING

We searched Medline via PubMed to identify systematic reviews of interventions including RCTs and OSs published in 110 journals from 2015 to 2019. We extracted in duplicate general and methodological characteristics of the systematic review.

RESULTS

We identified 402 systematic reviews. Only 39% (n=160) of them reported the availability of a pre-established protocol. A rationale for including observational data in the systematic review was clearly reported in 25% (n=102) of the systematic reviews. Thirty-two percent (n=130) of the reviews reported a search strategy intending to identify published and unpublished data for RCTs and OSs. The risk of bias of the individual studies was assessed in 89% (n=359) of the systematic reviews. In 74% (n=266) it was assessed for both RCTs and OSs; 180 (50%) used different tools. Information about confounding factors was reported in only 11% of systematic reviews and the type of effect estimates (crude or adjusted) used was specified in only 22% of the systematic reviews. Among the 385 systematic reviews that performed data synthesis, only 132 (33%) pooled OSs and RCTs in the same meta-analysis.

CONCLUSION

Including OSs in systematic reviews of interventions could provide useful information but such an approach could also be misleading; thus, several methodological details are needed to ensure appropriate handling of OS and valid results. Our study revealed, though, that substantial methodological information is missing in reports published in high impact factor general and specialty journals.

Waterpipe Use and Its Cardiovascular Effects: A Systematic Review and Meta-Analysis of Case-Control, Cross-Sectional, and Non-Randomized Studies

Approximately 100 million people globally smoke cigarettes, making it a significant and quickly spreading global tobacco epidemic. Substance use disorders are frequently evaluated by non-randomized studies. Tobacco use and its impacts on the cardiovascular system were the subjects of a comprehensive search across five electronic databases: Cochrane, MEDLINE, Scopus, Embase, and PubMed. The findings demonstrated that waterpipe smokers in comparison to non-smokers have immediate elevations in heart rate and blood pressure, lower levels of high-density lipoprotein, higher levels of low-density lipoprotein, higher levels of triglycerides, higher levels of fasting blood glucose, and a higher heart rate. Users of waterpipes and cigarettes had similar average heart rates, blood pressure, and lipid levels, with the exception that waterpipe smokers had greater total cholesterol. Smoking a waterpipe has significant negative effects on the cardiovascular system comparable to cigarette smoking, and non-randomized studies proved to yield substantial evidence related to its cardiovascular effects. Such study designs can be used to evaluate substance use and its cardiovascular impact.

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain.

Incorporating Baseline Outcome Data in Individual Participant Data Meta-Analysis of Non-randomized Studies

BACKGROUND

In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC).

METHODS

For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA.

RESULTS

Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score.

CONCLUSION

ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly.

High-dimensional generalized propensity score with application to omics data

Propensity score (PS) methods are popular when estimating causal effects in non-randomized studies. Drawing causal conclusion relies on the unconfoundedness assumption. This assumption is untestable and is considered more plausible if a large number of pre-treatment covariates are included in the analysis. However, previous studies have shown that including unnecessary covariates into PS models can lead to bias and efficiency loss. With the ever-increasing amounts of available data, such as the omics data, there is often little prior knowledge of the exact set of important covariates. Therefore, variable selection for causal inference in high-dimensional settings has received considerable attention in recent years. However, recent studies have focused mainly on binary treatments. In this study, we considered continuous treatments and proposed the generalized outcome-adaptive LASSO (GOAL) to select covariates that can provide an unbiased and statistically efficient estimation. Simulation studies showed that when the outcome model was linear, the GOAL selected almost all true confounders and predictors of outcome and excluded other covariates. The accuracy and precision of the estimates were close to ideal. Furthermore, the GOAL is robust to model misspecification. We applied the GOAL to seven DNA methylation datasets from the Gene Expression Omnibus database, which covered four brain regions, to estimate the causal effects of epigenetic aging acceleration on the incidence of Alzheimer's disease.

Comparison of statistical methodologies used to estimate the treatment effect on time-to-event outcomes in observational studies

The use of real-world data became more and more popular in the pharmaceutical industry. The impact of real-world evidence is now well emphasized by the regulatory authorities. Indeed, the analysis of this type of data can play a key role for treatment efficacy and safety. The aim of this work is to assess various methods and give guidance on the comparisons of drugs, mostly with respect to time-to-event data, in non-randomized studies with potentially confounding variables. For that purpose, several statistical methodologies are compared based on simulation studies. These methodologies belong to family classes of methods that are widely used for this type of problem: regression, matching, weighting and subclassification methods. The evaluation criteria used to compare methods performances are the relative bias, the mean square error, the coverage probability and the width of the confidence interval. In this paper, we consider different scenarios of dataset features in order to study the effect of the sample size, the number of covariates and the magnitude of the treatment effect on the statistical methodologies performances. These statistical analyses are conducted within a proportional hazard model framework. Furthermore, we highlight the advantage of using techniques to identify relevant covariates for time-to-event outcomes by comparing two variable selection methods under a frequentist and a Bayesian inference. Based on simulation results, recommendations on each of the family of methods are provided to guide decision making.

Clinical impact of neoadjuvant treatment in resectable pancreatic cancer: a systematic review and meta-analysis protocol

INTRODUCTION

Although the only curative strategy for pancreatic cancer is surgical resection, up to 85% of patients relapse after surgery. The efficacy of neoadjuvant treatment in resectable pancreatic cancer (RPC) remains unclear and there is no systematic review focusing fully on this issue. Recently, two prospective trials of neoadjuvant treatment in RPC were terminated early because of slow recruiting and existing randomised controlled trials (RCTs) have too small sample sizes. Therefore, to overcome probable biases, it would be more reasonable to include both RCTs and non-randomised studies (NRSs) with selected criteria. This review aims to investigate the effect of neoadjuvant chemotherapy (CTx) and chemoradiation therapy (CRT) in RPC using RCTs and specific NRSs.

METHOD AND ANALYSIS

This systematic review will include conventional RCTs as group I, and quasi-randomised controlled trials, non-randomised controlled trials and prospective cohort studies as group II. Two groups will be assessed and analysed separately. Comprehensive literature search will use Medline, Embase, Cochrane library and Scopus databases. Additionally, we will search references from relevant studies and abstracts from major conferences. Two authors will independently identify, screen, include studies, extract data and assess the risk of bias. Discrepancies will be resolved by consensus with another author. An independent methodologist will categorise and assess NRSs to minimise heterogeneity. In each study group, meta-analysis will be conducted using a random-effect model and statistical heterogeneity will be evaluated using I(2)-statistics. Publication bias will be visualised with contour-enhanced funnel plots and analysed with Egger's test. In group I, cumulative meta-analysis will be considered because the CTx regimen and CRT protocol have changed. The quality of evidence will be summarised using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach.

ETHICS AND DISSEMINATION

This review does not use primary data, and formal ethical approval is not required. Findings will be disseminated through peer-reviewed journals and committee conferences.

TRIAL REGISTRATION NUMBER

CRD42015023820.

Tocolysis for inhibiting preterm birth in extremely preterm birth, multiple gestations and in growth-restricted fetuses: a systematic review and meta-analysis

This systematic review was to identify available evidence on the effectiveness of tocolysis in inhibiting preterm delivery for women with threatened extremely preterm birth, multiple gestations, and growth-restricted babies, and their infants' outcomes. A comprehensive search using MEDLINE, Embase, the Cochrane Library, CINAHL, POPLINE and the WHO Global Health Library databases was conducted on 14 February 2014. For selection criteria, randomized controlled trials and non-randomized studies that compared tocolysis treatment to placebo or no treatment were considered. Selection of eligible studies, critical appraisal of the included studies, data collection, meta-analyses, and assessment of evidence quality were performed in accordance with the Cochrane Collaboration's guidance and validated assessment criteria. The search identified seven studies for extremely preterm birth, in which three were randomized controlled trials (RCTs) and four were non-randomized studies (non-RCTs). There were no eligible studies identified for women with multiple pregnancy and growth-restricted fetuses. Meta-analyses indicated no significant difference was found for the relative effectiveness of tocolytics versus placebo for prolonging pregnancy in women with extremely preterm birth (RR 1.04, 95% CI 0.83 to 1.31) or reducing the rate of perinatal deaths (RR 2.22, 95% CI 0.26 to 19.24). In summary, there is no evidence to draw conclusions on the effectiveness of tocolytic therapy for women with threatened extremely preterm birth, multiple gestations, and growth-restricted babies.

Efficacy and safety of pharmacological treatments for neuroborreliosis--protocol for a systematic review

BACKGROUND

Neuroborreliosis is a tick-borne infectious disease of the nervous system caused by Borrelia burgdorferi. Common clinical manifestations of neuroborreliosis are cranial nerve dysfunctions, polyradiculoneuritis, and meningitis. Diagnosis is usually based on clinical presentation, serologic testing, and analysis of cerebrospinal fluid. Many aspects of pharmacological treatment, such as choice of drug, dosage, and duration are subject of intense debate, leading to uncertainties in patients and healthcare providers alike. To approach the questions regarding pharmacological treatment of neuroborreliosis, we will perform a systematic review.

METHODS

We will perform a comprehensive systematic literature search for potentially eligible studies that report outcomes after pharmacological interventions. To adequately consider the wealth of research that has been conducted so far, this review will evaluate randomized controlled trials (RCTs) and non-randomized studies on treatment of neuroborreliosis. We will assess potential risk of bias for each RCT meeting our selection criteria using the Cochrane risk of bias tool for RCTs. For non-randomized studies, we will use the Newcastle-Ottawa Scale and the recently piloted Cochrane risk of bias tool for non-randomized studies. Our primary outcome of interest will be neurological symptoms and the secondary outcomes will be disability, patient-reported outcomes (quality of life, and, if reported separately from other neurological symptoms, pain, fatigue, depression, cognition, and sleep), adverse events, and cerebrospinal fluid pleocytosis. Pooling of data and meta-analysis will only be deemed justified between studies with similar design (e.g., RCTs are only combined with other RCTs), characteristics (e.g., similar populations), and of acceptable heterogeneity (I2 < 80%). Pooled estimates will be calculated using RevMan software. Prespecified subgroup analyses will evaluate groups of antibiotics, length of antibiotic treatment, and different doses of doxycycline. We will assess the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

DISCUSSION

This systematic review will summarize the available evidence from RCTs and non-randomized studies regarding pharmacological treatment of neuroborreliosis. The available evidence will be summarized and discussed to provide a basis for decision-making for patients and healthcare professionals.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number: CRD42014008839.

Observational evidence and strength of evidence domains: case examples

BACKGROUND

Systematic reviews of healthcare interventions most often focus on randomized controlled trials (RCTs). However, certain circumstances warrant consideration of observational evidence, and such studies are increasingly being included as evidence in systematic reviews.

METHODS

To illustrate the use of observational evidence, we present case examples of systematic reviews in which observational evidence was considered as well as case examples of individual observational studies, and how they demonstrate various strength of evidence domains in accordance with current Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) methods guidance.

RESULTS

In the presented examples, observational evidence is used when RCTs are infeasible or raise ethical concerns, lack generalizability, or provide insufficient data. Individual study case examples highlight how observational evidence may fulfill required strength of evidence domains, such as study limitations (reduced risk of selection, detection, performance, and attrition); directness; consistency; precision; and reporting bias (publication, selective outcome reporting, and selective analysis reporting), as well as additional domains of dose-response association, plausible confounding that would decrease the observed effect, and strength of association (magnitude of effect).

CONCLUSIONS

The cases highlighted in this paper demonstrate how observational studies may provide moderate to (rarely) high strength evidence in systematic reviews.

Dementia Prevention: optimizing the use of observational data for personal, clinical, and public health decision-making

Worldwide, over 35 million people suffer from Alzheimer's disease and related dementias. This number is expected to triple over the next 40 years. How can we improve the evidence supporting strategies to reduce the rate of dementia in future generations? The risk of dementia is likely influenced by modifiable factors such as exercise, cognitive activity, and the clinical management of diabetes and hypertension. However, the quality of evidence is limited and it remains unclear whether specific interventions to reduce these modifiable risk factors can, in turn, reduce the risk of dementia. Although randomized controlled trials are the gold-standard for causality, the majority of evidence for long-term dementia prevention derives from, and will likely continue to derive from, observational studies. Although observational research has some unavoidable limitations, its utility for dementia prevention might be improved by, for example, better distinction between confirmatory and exploratory research, higher reporting standards, investment in effectiveness research enabled by increased data-pooling, and standardized exposure and outcome measures. Informed decision-making by the general public on low-risk health choices that could have broad potential benefits could be enabled by internet-based tools and decision-aids to communicate the evidence, its quality, and the estimated magnitude of effect.

Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions

Non-randomized studies may provide valuable evidence on the effects of interventions. They are the main source of evidence on the intended effects of some types of interventions and often provide the only evidence about the effects of interventions on long-term outcomes, rare events or adverse effects. Therefore, systematic reviews on the effects of interventions may include various types of non-randomized studies. In this second paper in a series, we address how review authors might articulate the particular non-randomized study designs they will include and how they might evaluate, in general terms, the extent to which a particular non-randomized study is at risk of important biases. We offer guidance for describing and classifying different non-randomized designs based on specific features of the studies in place of using non-informative study design labels. We also suggest criteria to consider when deciding whether to include non-randomized studies. We conclude that a taxonomy of study designs based on study design features is needed. Review authors need new tools specifically to assess the risk of bias for some non-randomized designs that involve a different inferential logic compared with parallel group trials. Copyright © 2012 John Wiley & Sons, Ltd.

A tutorial on methods to estimating clinically and policy-meaningful measures of treatment effects in prospective observational studies: a review

In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to be directly compared between treatment arms. When outcomes are binary, the effect of treatment can be summarized using relative risks, absolute risk reductions and the number needed to treat (NNT). When outcomes are time-to-event in nature, the effect of treatment on the absolute reduction of the risk of an event occurring within a specified duration of follow-up and the associated NNT can be estimated. In observational studies of the effect of treatments on health outcomes, treatment is frequently confounded with baseline covariates. Regression adjustment is commonly used to estimate the adjusted effect of treatment on outcomes. We highlight several limitations of measures of treatment effect that are directly obtained from regression models. We illustrate how both regression-based approaches and propensity-score based approaches allow one to estimate the same measures of treatment effect as those that are commonly reported in RCTs. The CONSORT statement recommends that both relative and absolute measures of treatment effects be reported for RCTs with dichotomous outcomes. The methods described in this paper will allow for similar reporting in observational studies.